Abstract
Within the framework of aging materials inspection, one of the most important aspects regards defects detection in metal welded strips. In this context, it is important to plan a method able to distinguish the presence or absence of defects within welds as well as a robust procedure able to characterize the defect itself. In this paper an innovative solution that exploits a rotating magnetic field is presented. This approach has been carried out by a Finite Element Model. Within this framework, it is necessary to consider techniques able to offer advantages in terms of sensibility of analysis, strong reliability, speed of carrying out, low costs: its implementation can be a useful support for inspectors. To this aim, it is necessary to solve inverse problems which are mostly ill-posed: in this case, the main problems consist on both the accurate formulation of the direct problem and the correct regularization of the inverse electromagnetic problem. In the last decades, a useful and very performing way to regularize ill-posed inverse electromagnetic problems is based on the use of a Neural Network approach, the so called “learning by sample techniques”.
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References
Grimberg, R., Radu, E., Mihalache, O., Savin, A.: Calculation of the induced electromagnetic field created by an arbitrary current distribution located outside a conductive cylinder. J. Phys. D: Appl. Phys. 30, 2285–2291 (1997)
Savin, A., Grimberg, R., Mihalache, O.: Analytical solutions describing the operation of a rotating magnetic field transducer. IEEE Transactions on Magnetics 33, 697–701 (1997)
Wikipedia, T.F.E.: Rotating magnetic field (2008), http://en.wikipedia.org/wiki/Rotating_magnetic_field
Takagi, T., Hashimoto, M., Fukutomi, H., Kurokawa, M., Miya, K., Tsuboi, H.: Benchmark models of eddy current testing for steam generator tube: experiment and numerical analysis. Int. J. Appl. Elect. Mater. 4, 149–162 (1994)
Bowler, J.: Eddy current interaction with an ideal crack: I. the forward problem. J. Appl. Phys. 75, 8128–8137 (1994)
Takagi, T., Huang, H., Fukutomi, H., Tani, J.: Numerical evaluation of correlation between crack size and eddy current testing signals by a very fast simulator. IEEE Transactions on Magnetics 34, 2582–2584 (1998)
Kohavi, R.: A study of cross-validation and bootstrap for accuracy estimation and model selection. In: Proceedings of the 14th International Joint Conference on Artificial Intelligence, pp. 1137–1143 (1995)
Lewis, A.M.: A theoretical model of the response of an eddy-current probe to a surface-breaking metal fatigue crack in a flat test piece. J. Appl. Phys. 25, 319–326 (1992)
Trevisan, F., Kettunen, L.: Geometric interpretation of discrete approaches to solving magnetostatics. IEEE Transaction on Magnetics 40, 361–365 (2004)
Rodger, D., Leonard, P., Lai, H.: Interfacing the general 3d a-ψ method with a thin sheet conductor model. IEEE Transactions on Magnetics 28, 1115–1117 (1992)
Tsuboi, H., Misaki, T.: Three dimensional analysis of eddy current distribution by the boundary element method using vector variables. IEEE Transactions on Magnetics 23, 3044–3046 (1987)
Weissenburger, D., Christensen, U.: A network mesh method to calculate eddy currents on conducting surfaces. IEEE Transactions on Magnetics 18, 422–425 (1982)
Weisstein, E.: Euler angles. mathworld, a wolfram web resource (2008), http://mathworld.wolfram.com/EulerAngles.html
Bishop, C.: Neural Networks for Pattern Recognition. Oxford University Press, Oxford (1996)
Gurney, K.: An Introduction to Neural Networks. Routledge, London(1997)
Haykin, S.: Neural Networks: A Comprehensive Foundation, 2nd edn. Prentice-Hall, London (1999)
Lawrence, J.: Neural Networks for Pattern Recognition. Oxford University Press, Oxford (1996)
Wasserman, P.: Introduction to Neural Networks: Design, Theory and Applications, 6th edn. California Scientific Software Press, San Francisco (1994)
Daubechies, I.: Ten Lectures on Wavelets. Society for Industrial and Applied Mathematics, Philadelphia (1992)
Jolliffe, I.: Principal component analysis. Springer Series in Statistics. Springer, Heidelberg (2002)
Powell, W.: Approximate Dynamic Programming: Solving the Curses of Dimensionality. Wiley Press, New York (2007)
Kurková, V.: Kolmogorov’s theorem and multilayer neural networks. Neural Networks 5, 501–506 (1992)
Draper, N., Smith, H.: Applied Regression Analysis. Wiley-Interscience, New York (1998)
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Cacciola, M. et al. (2009). Advanced Integration of Neural Networks for Characterizing Voids in Welded Strips. In: Alippi, C., Polycarpou, M., Panayiotou, C., Ellinas, G. (eds) Artificial Neural Networks – ICANN 2009. ICANN 2009. Lecture Notes in Computer Science, vol 5769. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04277-5_46
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DOI: https://doi.org/10.1007/978-3-642-04277-5_46
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